Method for manufacturing semiconductor devices and method and its apparatus for processing detected defect data

ABSTRACT

Disclosed herein is a method for manufacturing semiconductor device and a method and apparatus for processing detected defect data, making it possible to quickly infer or determine a process and related manufacturing equipment that causes defects in a fabrication line of semiconductor devices, take remedy action, and achieve a constant and high yield. The method of the invention comprises quantitatively evaluating similarity of a defects distribution on a wafer that suffered abnormal occurrence of defects to inspection results for wafers inspected in the past, analyzing cyclicity of data sequence of evaluated similarity, evaluating relationship between the cyclicity of defects obtained from the analysis and the process method according to each manufacturing equipment in the fabrication line, and inferring or determining a causal process and equipment that caused the defects.

BACKGROUND OF THE INVENTION

The present invention relates to a method for processing detected defectdata obtained by inspecting semiconductor substrates processed until apredetermined fabrication sub-process, with an inspection equipment or areview station, for defects or suspected defects occurred to thesemiconductor substrates, in a fabrication line consisting of aplurality of fabrication sub-processes arranged in sequence, eachsub-process being performed by one or a plurality of manufacturingequipments. In particular, the invention relates to a method formanufacturing semiconductor devices, while controlling fault occurrencein the sub-processes in the fabrication line.

As is shown in FIG. 1, a semiconductor device manufacturing processconsists of a great number of pattern-forming processes that areiteration. Each pattern-forming process (major process) is comprised ofthe processes (sub-processes) of thin-film deposition, resistapplication, expose resist, develop resist, etching, remove resist, andcleaning. Unless the manufacturing conditions are optimized in all theabove processes, circuit patterns of a semiconductor device are notformed properly and cracks or deformation may take place in thepatterns, which results in wafers that are not acceptable as products.

After being fabricated on a wafer, semiconductor devices areelectrically inspected. If defects are detected, examination is made toascertain what caused them by an appropriate method such as a failurebitmap analysis and remedy action is taken. For example, a known methodis described in JP-A No. 354396/1999 that statistically analyzes therelation between the electrical characteristics of the completed deviceand processing equipments, and determines what equipment caused thedefect.

However, the problem with this method is that, even if defects haveoccurred in the course of the fabrication process, the defects cannot bedetected until the fabrication of the wafer is completed. Thus, it mayhappen that a great quantity of defective products are made beforeappropriate action against the defects is taken.

To address this problem, the following method has widely been used. Asshown in FIG. 1, semiconductor wafers in process of fabrication areinspected for critical dimensions, patterns, and particles on thesurface of a wafer, the cause of defects that may occur due to failureor malfunction of an equipment is investigated, and remedy action istaken against it. However, the addition of such inspection processesbecomes obstructive to shortening the manufacturing time and thereforeit is impossible to inspect all products in all processes. Accordingly,in the practical fabrication line of semiconductor devices, such amethod is taken that inspection applies only to critical processes orthat wafers to be inspected are sampled at a rate, for example, one forseveral lots.

Because all manufacturing processes shown in FIG. 1 are possible to bethe source of defects, an important technical issue has focused on howto determine a particular process and equipment that caused defects,using the result data from inspection for a small number of wafers.

JP-A No. 455919/1999 discloses a known method of inferring a processthat caused defects from defects distribution data obtained by theinspection of wafers in process of fabrication. The problem with thismethod is that it is difficult to determine a particular process andequipment as the source of defects in the critical processes for whichthe inspection was not performed.

It is also known that defects having the same causal relationship aresimilar in their spatial properties and, from this fact, spatialclustering of defects is performed correctly, according to thespecification and its accompanying drawings of U.S. Pat. No. 5,991,699.

From the analysis of inspection result data, using the above priortechniques, a sub-process or manufacturing equipment that caused defectscan be inferred to some degree. However, it is very difficult topinpoint a particular sub-process as the source of the defects becausesemiconductor devices are manufactured through quite a great number ofmanufacturing processes and there are many sub-processes between aprocess under inspection and the next process under inspection.

If a plurality of defects resulting from different causes occur,coexisting on a wafer, these defects are interactive and make analysiscomplex and it often takes considerable time to investigate the cause ofthe defects.

The previous data analysis methods are all unable to predict wheredefects would be likely to occur.

SUMMARY OF THE INVENTION

The present invention provides a method for manufacturing semiconductordevices and a method and apparatus for processing detected defect data,thereby making the following possible: for various types of defects thatoccur to semiconductor substrates during a fabrication line formanufacturing semiconductor devices, early inferring or determining aparticular sub-process and related manufacturing equipment as the sourceof the defects and taking remedy action against the detects at an earlystage.

According to one aspect of the invention, a method for manufacturingsemiconductor devices is provided. The method comprises: an inspectionstep of inspecting semiconductor substrates processed until apredetermined sub-process for defects or suspected defects occurred tothe semiconductor substrates with an inspection equipment in afabrication line consisting of a plurality of sub-processes arranged insequence, each sub-process being performed by one or a plurality ofmanufacturing equipments; and a step of collecting and analyzinginspection results. The step of collecting and analyzing inspectionresults comprises: a step of generating a defects distribution on asemiconductor substrate in order to investigate the cause of thedefects, based on the inspection results obtained from the inspectionstep; a step of quantitatively evaluating defect features of the defectsdistribution on a semiconductor substrate generated by the step ofgenerating a defects distribution; a step of generating data of thedefect features quantitatively evaluated by the step of quantitativelyevaluating defect features for the manufacturing equipments thatprocessed the semiconductor substrates and the sub-processes thatapplied to the semiconductor substrates; and a causation inferring stepof evaluating cyclicity of the defect features data for themanufacturing equipments that processed the semiconductor substrates andthe sub-processes that applied to the semiconductor substrates,generated by the step of generating data, thereby inferring a causalmanufacturing equipment that caused the defects.

According to another aspect of the invention, a method for processingdetected defect data is provided. The method comprises: a step ofgenerating a defects distribution on a semiconductor substrate in orderto investigate the cause of the defects by processing detected defectdata obtained by inspecting semiconductor substrates processed until apredetermined sub-process with an inspection equipment for defects orsuspected defects occurred to the semiconductor substrates in afabrication line consisting of a plurality of sub-processes arranged insequence, each sub-process being performed by one or a plurality ofmanufacturing equipments; a step of quantitatively evaluating defectfeatures of the defects distribution on a semiconductor substrategenerated by the step of generating a defects distribution; a step ofgenerating data of the defect features quantitatively evaluated by thestep of quantitatively evaluating defect features for the manufacturingequipments that processed the semiconductor substrates and thesub-processes that applied to the semiconductor substrates; and acausation inferring step of evaluating cyclicity of the defect featuresdata for the manufacturing equipments that processed the semiconductorsubstrates and the sub-processes that applied to the semiconductorsubstrates, generated by the step of generating data, thereby inferringa causal manufacturing equipment that caused the defects.

According to a further aspect of the invention, an apparatus forprocessing detected defect is provided. The apparatus comprises: aportion for generating a defects distribution on a semiconductorsubstrate in order to investigate the cause of the defects, based ondetected defect data obtained by inspecting semiconductor substratesprocessed until a predetermined sub-process with an inspection equipmentfor defects or suspected defects occurred to the semiconductorsubstrates in a fabrication line consisting of a plurality ofsub-processes arranged in sequence, each sub-process being performed byone or a plurality of manufacturing equipments; a portion forquantitatively evaluating defect features of the defects distribution ona semiconductor substrate generated by the portion for generating adefects distribution; a portion for generating data of the defectfeatures quantitatively evaluated by the portion for quantitativelyevaluating defect features for the manufacturing equipments thatprocessed the semiconductor substrates and the sub-processes thatapplied to the semiconductor substrates, and a causation inferringportion for evaluating cyclicity of the defect features data for themanufacturing equipments that processed the semiconductor substrates andthe sub-processes that applied to the semiconductor substrates,generated by the portion for generating data, thereby inferring a causalmanufacturing equipment that caused the defects.

These and other objects, features and advantages of the invention willbe apparent from the following more particular description of preferredembodiments of the invention, as illustrated in the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram for explaining a semiconductor device manufacturingprocess;

FIG. 2 is a, diagram for explaining sub-processes in a fabricationprocess of semiconductor devices, each sub-process using a plurality ofmanufacturing equipments;

FIG. 3 is a diagram showing a system configuration of a semiconductorfabrication line, according to a preferred embodiment of the invention;

FIG. 4A illustrates defects distribution detected on a wafer;

FIG. 4B illustrates past defects distributions, which explains, incombination of FIG. 4A, evaluation of a degree of matching between thedefects distribution of FIG. 4A and the defects distributions of FIG.4B, according to a preferred embodiment of the invention;

FIG. 5A is a graph representing cyclic occurrence of defective wafers;

FIG. 5B illustrates the structure of a manufacturing equipment;

FIG. 5C illustrates the structure of another manufacturing equipment;from the relationship between the equipment structure and the cyclicdefect occurrence of FIG. 5A, the defect source is inferred, accordingto a preferred embodiment of the invention;

FIG. 6 is a flowchart of a procedure for processing detected defectdata, according to a preferred embodiment of the invention;

FIG. 7A illustrates an example of a defect map obtained, according tothe invention;

FIG. 7B illustrates another example of a defect map obtained, accordingto the invention;

FIG. 7C illustrates an example of a defects distribution which isspecified (or generated) to help investigate the cause of the defects,according to the invention;

FIG. 7D illustrates another example of the above-mentioned defectsdistribution;

FIG. 7E illustrates a further example of the above-mentioned defectsdistribution;

FIG. 8A illustrates an example of distribution of defects obtained frominspection results, which is classified by defect type, according to theinvention;

FIG. 8B illustrates a defect type of particles;

FIG. 8C illustrates a defect type of scratches;

FIG. 8D illustrates a defect type of pattern defects;

FIG. 9A illustrates a defects distribution whose degree of matching(similarity) with defects distributions from a database is calculated,according to the invention;

FIG. 9B illustrates examples of defects distributions from a database;

FIG. 10A shows a graph of matching (similarity) degree vs. equipmentrelationship in a sub-process;

FIG. 10B shows a graph of matching (similarity) degree vs. equipmentrelationship in another sub-process;

FIG. 10C shows a graph of evaluation values vs. sub-processrelationship, which is used in combination with FIGS. 10A and 10B forextracting sub-processes as likely sources of defects, according to theinvention;

FIG. 11A shows a graph plotting an example of matching (similarity)degree data sequence, according to the invention;

FIG. 11B shows a graph plotting another example of matching (similarity)degree data sequence, according to the invention;

FIG. 11C shows a graph plotting yet another example of matching(similarity) degree data sequence, according to the invention;

FIG. 12 shows a graph plotting a further example of matching(similarity) degree data sequence, according to the invention;

FIG. 13 is a diagram for explaining how to select wafers to beinspected, according to the invention;

FIG. 14 shows a table of exemplary equipment data for use in theinvention;

FIG. 15 consists of FIGS. 15A, 15B, 15C, 15D, 15E, and 15D showingexamples of defects distributions which are generally found;

FIG. 16 is a flowchart of a procedure for evaluating defectsdistributions, according to the invention;

FIG. 17 illustrates an exemplary form of defects data stored, accordingto the invention;

FIG. 18 consists of FIGS. 18A, 18B, and 18C showing examples of defectsdistributions which occur in different positions on a wafer;

FIG. 19 illustrates an exemplary database of defect instances, accordingto the invention; and

FIG. 20 illustrates an example of presentation of analysis results,according to the invention.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Preferred embodiments of the method for manufacturing semiconductordevices and the manufacturing process management method according to theinvention will now be described, using the accompanying drawings.

As shown in FIG. 1, a semiconductor device is manufactured, beginningwith the process of surface coating of a wafer with oxide film andthrough a great number of major processes of pattern forming 2 to Mwhich are iteration as many times as the number of layers. A majorprocess of pattern forming m essentially corresponds to one layer and iscomprised of the following: a sub-process of thin-film deposition 11 fordepositing conductive film and insulation film, using a depositionequipment such as a sputtering deposition equipment or CVD depositionequipment; a sub-process of resist application 12 for applying resistfilm to the conductive film and insulation film deposited by thesub-process of thin-film deposition 11, using a resist applicationequipment; a sub-process of expose resist 13 for exposing the resist tolight that passes through a circuit pattern mask, using a stepper usingi beams, an excimer laser exposure equipment, or the like; a sub-processof develop resist pattern 14 for developing the circuit patterntransferred onto the resist film; a sub-process of etching 15 foretching the conductive film and insulation film according to the resistcircuit pattern developed by the sub-process of develop resist pattern14, using an etching equipment, thereby forming the circuit pattern; asub-process of remove resist 16 for removing the remaining resist; and asub-process of cleaning 17 for cleaning the surface of the wafer.

The wafer being thus processed is inspected after criticalsub-processes. For example, particle inspection 21: after beingprocessed by the sub-process of thin-film deposition 11, the wafer(semiconductor substrate) is sampled and inspected for particlesdeposited during the thin-film deposition sub-process with a particleinspection equipment. Critical dimensions measurement: after beingprocessed by the sub-process of develop resist pattern 14, the wafer issampled and inspected to confirm whether the circuit pattern has beentransferred onto the resist correctly with a critical dimensionsmeasuring equipment. Pattern inspection 23: after being processed by thesub-process of remove resist 16, the wafer is sampled and inspected fordefects such as short-circuits and breaks in the circuit pattern formedwith the conductive film and insulation film, using a pattern inspectionequipment.

FIG. 2 shows arrangement of sub-processes under a major process ofpattern-forming for a semiconductor device, wherein the sub-processesare more detailed subdivision than those of a major process of patternforming shown in FIG. 1 and shown with the manufacturing equipments thatare used in each sub-process. Here, the sub-processes 1 to N shown inFIG. 2 include inspection processes with regard to particles, patterndefects, and dimensions in addition to the manufacturing processes suchas thin-film deposition, exposure, etching, and cleaning. For theinspection processes, the associated equipments are inspectionequipments or dimensions measuring equipments, not manufacturingequipments.

In ordinary semiconductor manufacturing processes, even the samesemiconductor device is manufactured with a plurality of manufacturingequipments across a multiplicity of sub-processes as shown in FIG. 2. Ina major process of pattern forming, for example, a wafer is fabricatedwith manufacturing equipment (1-1) in the first sub-process (1),manufacturing equipment (2-2) in the second sub-process (2), andmanufacturing equipment (3-3) in the third sub-process 3. In furthersub-processes (n−5) to (n−2), the wafer is fabricated with manufacturingequipments ((n−5)−2), ((n−4)−2), ((n−3)−2), ((n−2)−1), and ((n−2)−2),respectively. In a further sub-process (n), the wafer is fabricated withmanufacturing equipment (n−1). In further sub-processes (n+1) to (n−3),the wafer is fabricated with manufacturing equipments ((n+1)−2) and((n+2)−2) respectively. In the final sub-process (N), the wafer isfabricated with manufacturing equipment (N−1).

For example, if the sub-process n shown in FIG. 2 is an inspectionsub-process, the source of a defect of the wafer detected by theinspection sub-process n is regarded as lying in any of the upstreammanufacturing equipments ((n−2)−2) to ((n−5)−2) . . . (3-3), (2-2), and(1-1) involved in the fabrication of the wafer. This shows that the samedefect may occur in other wafers fabricated with that same upstreammanufacturing equipment as the source of the defect.

To reduce defects in fabricated wafers, when a fault on a wafer is foundby inspection, it is important to identify which of the upstreammanufacturing equipments that is the source of the fault as soon aspossible and take remedy action for the equipment.

The invention is embodied as a semiconductor fabrication line managementsystem making it possible to identify a particular sub-process andmanufacturing equipment used therein that is the source of a defectquickly and take remedy action, using defect types, distribution ofdefects, and time sequence information obtained by inspection.

The embodiment of the semiconductor fabrication line management systemaccording to the invention is configured for a major process of patternforming as is shown in FIG. 3. On the semiconductor fabrication line,major processes of pattern forming are iterated. Sub-process (1) isimplemented with manufacturing equipments (1-1) and (1-2). Sub-process(n−2) is implemented with manufacturing equipment ((n−2)−1). Sub-process(n−1) is implemented with manufacturing equipments ((n−1)−1) to((n−1)−3). Sub-process (n) which is an inspection sub-process isimplemented with inspection equipments (which may include a reviewstation) (n−1) and (n−2). Sub-process (N) is implemented withmanufacturing equipments (N−1) and (N−2). A fabrication line managementstation 34 to which all manufacturing equipments (including those fordeposition such as sputters and CVD, exposure equipment and etchingequipment) are connected via a network or supply data via recordingmedia collects equipment data 35 that represents manufacturingconditions (manufacturing process conditions) including theconfiguration and history of each manufacturing equipment on thefabrication line.

A fabrication history management unit 32 manages the fabrication historyof the sub-processes (1) to (N) of a wafer 1. By reading the productnumber of the wafer 1 in each sub-process, the fabrication historymanagement unit stores fabrication history data into its storage (notshown).

An inspection results collection and analysis unit 30 that carries outdata processing in accordance with the invention receives defectsdistribution or defects variation data 31 per type of defects thatoccurred to wafers inspected in the past from the inspection equipments(which may include the review station). The inspection resultscollection and analysis unit collects the above defects distribution ordefects variation data 31 per defect type, the fabrication process data33 for the wafers, sent from the fabrication history management unit 32,and the equipment data 35 that represents manufacturing conditions(manufacturing process conditions) including the configuration andhistory of each manufacturing equipment, sent from the fabrication linemanagement station 34, and stores a multiplicity of defects that arepossible to be judged faulty in a form of defects distribution data ordefects variation data per defect type into a past fault database 39. Inthe past fault database 39, thus, defects distribution data or defectsvariation data per defect type from a multiplicity of defects is storedbeforehand as will be shown in FIG. 4B and FIG. 7B, which is used asreference data for evaluating defects.

For each wafer (which may be each sampled wafer) flowing on thefabrication line, the inspection results collection and analysis unit 30receives defects distribution or defects variation data 31 per defecttype inspected by the inspection equipment or review station; collectsthe defects distribution or defects variation data 31 per defect type,the fabrication process data 33 per wafer, sent from the fabricationhistory management unit 32, and the equipment data 35 that representsmanufacturing conditions (manufacturing process conditions) includingthe configuration and history of each manufacturing equipment, sent fromthe fabrication line management station 34; and compares the collecteddefects distribution or defects variation data 31 per defect type withthe reference data per defect type from a multiplicity of past defectsstored in the past fault database 39 for matching to a degree. Inaddition, by analyzing defects data, using the fabrication process data33 per wafer and the equipment data 35 in combination, the inspectionresults collection and analysis unit 30 infers what manufacturingsub-process and manufacturing equipment used therein are the sources ofdefects per type of defects judged abnormal.

Once the inspection results collection and analysis unit 30 hasdetermined a faulty manufacturing equipment, it sends fault informationon the manufacturing equipment to a fault monitoring unit 36 and thefabrication line management station 34.

The fault monitoring unit 36 conveys the fault information received fromthe inspection results collection and analysis unit 30 to a fabricationline administrator 37 via display and annunciator means 38. On the otherhand, the fabrication line management station 34 exerts control such asstopping the on-going fabrication process by the faulty manufacturingequipment and switches the operation to another manufacturing equipmentthat operates normally, based on the fault information received from theinspection results collection and analysis unit 30. If the manufacturingequipment can recover from the fault by altering the process conditionsthereof, the fabrication line management station, of course, exertscontrol for adjusting the process conditions.

The invention will be described in further detail. As the result ofinspection by the inspection equipment (n−1), suppose that, for example,a great quantity of defects such as particles have occurred, distributedon a wafer as is illustrated by a case D1 in FIG. 4. The point theinvention aimed at is that, if, among the wafers inspected by theinspection equipment in the past, there is a wafer on which the sametype defects such as particles having the same or similar characteristicdistribution occurred, it can be inferred that the defects such asparticles having the same causal relationship would occur on the wafer.

Specifically, the invention is embodied such that the inspection resultscollection and analysis unit 30 compares the defects distribution D1showing abnormal occurrence of defects on the wafer detected by theinspection equipment or review station (n−1) shown in FIG. 4A with theinspection results (past defects distributions) R1 to Rn of the wafersinspected in the past, shown in FIG. 4(b), which have been stored in thepast fault database 39, evaluates a degree of matching quantitatively,and determines what sub-process and manufacturing equipment used thereinis likely to cause the defects, using the evaluation value. In the caseof FIG. 4, such quantitative evaluation is possible that the defectsdistribution D1 showing abnormal occurrence of defects on the wafermatches with the past defects distributions R1 and R3 at high degrees.Thus, the spectrum of inferring the source can be narrowed down to thesub-processes and related manufacturing equipments that caused the pastdefects distributions R1 and R3.

For example, for wafers of one lot, suppose that, from the results ofevaluation of a degree of matching of the defects distribution D1showing abnormal occurrence of defects on a wafer shown in FIG. 4A withthe past defects distributions, a graph in FIG. 5A is obtained (how toevaluate a degree of matching will be described later).

If, among the manufacturing equipments engaged in fabricating thesewafers, the manufacturing equipment ((n−2)−1) for the sub-process (n−2)and the manufacturing equipment ((n−1)−2) for the sub-process (n−1) arelikely sources of defects, which equipment is the source is identifiedin the following manner. For example, the sub-process (n−2) is heattreatment and several tens of wafers 6 are simultaneously loaded intoits heat treatment furnace (vertical furnace) 5 as is shown in FIG. 5B.

On the other hand, the sub-process (n−1) is CVD process and theequipment for the process ((n−1)−2) has two chambers as is shown in FIG.5C and the wafers in the lot are alternately loaded into chamber A 7 andchamber B 8. From this, the CVD equipment shown in FIG. 5C can beinferred to cause a great number of defects occurring every second waferas shown in FIG. 5A. To the CVD equipment, odd-numbered wafers areloaded into chamber A and even-numbered wafers are loaded into chamberB, the process in the chamber B can be identified as the source of thegreat number of defects. Thus, what to do is investigating the cause ofthe defects for the chamber B. During the investigation and remedyaction, the sub-process can be carried out, using another CVD equipment.The sub-process can be carried out with chamber A only if circumstancespermit.

In the invention, the inspection results collection and analysis unit 30calculates the frequency of cyclic occurrence of defective wafers byfrequency analysis; one defective wafer occurs per two wafers in thisembodiment. Moreover, from the inspection results collection andanalysis unit 30, the operator can register in advance the units ofwafers to be processed at a time by the equipment for all manufacturingequipments (in the above-described embodiment, the number of chambers ofthe CVD equipment as equipment data and the number of wafers to beloaded into the furnace as wafer process data) 33 and 35, which arestored into the fabrication history management unit 32 and thefabrication line management station 34. By receiving the thus storeddata 33 and 35 and using them in combination with the inspection resultsdata, the inspection results collection and analysis unit 30 canidentify a particular manufacturing equipment that is the source of thedefects in the manner described above. If the inspection resultscollection and analysis unit 30 can quickly determine what manufacturingequipment and related sub-process caused the defects in this manner, thefault information is presented to the fabrication line managementstation 34 and the fabrication line administrator 37. By stopping thefabrication process by the faulty equipment and sub-process,unacceptable wafers resulting from any fault in the manufacturingprocess can be minimized.

Then, how the inspection results collection and analysis unit 30analyzes the inspection results data will be described in furtherdetail, using FIG. 6.

The inspection results collection and analysis unit 30 first presentsdefect maps data (defects distributions) identifiable per defect typereceived from the inspection equipment or review station (n−1) or (n−2)to its operator; for example, displaying that data on its display device40. According to the operator's judgment, specify a defect type andrelated defect map for which the cause of abnormal defects need to beinvestigated, using input means 41 (step S61). If a criterion forabnormal defects judgement is set beforehand, it is also possible thatthe inspection results collection and analysis unit 30 automaticallyselects and designates a defect type and related defect map for whichthe cause of the defects need to be investigated from among the defectsdistribution or defects variation data 31 per defect type received fromthe inspection equipment or review station (n−1) or (n−2). For, example,if the number of defects obtained from the inspection results receivedfrom the inspection equipment or review station (n−1) or (n−2) exceeds avalue that is critical for management, as is illustrated in FIG. 7A, theinspection results collection and analysis unit 30 automatically selectsthe map of such defects for which the cause of the defects should beinvestigated, using the defects distribution.

In the alternative, it may be also possible that the inspection resultscollection and analysis unit 30 selects a defects distribution to beanalyzed, obtained by merging the inspection results received from theinspection equipment or review station (n−1) or (n−2) and pastinspection results so that newly detected defects are superposed on thepast defects distribution, as is illustrated in FIG. 7B.

By thus merging the inspection results received from the inspectionequipment or review station (n−1) or (n−2) and the past inspection datainto a map of defects, it is possible to detect defects that do notoccur in quantity, but constantly happen and investigate the cause ofsuch defects. In this case, before specifying a defects distribution tobe analyzed, it is necessary to exclude the data for a wafer on which agreat number of defects apparently occur.

In the step S61, specifying a defects distribution to be analyzed may becarried out such that, on a map of defects displayed on the displaydevice 40, as is illustrated in FIG. 7, the operator may set a target ofanalysis, using software for drawing a figure like the one shown in FIG.7C, or a region having defects of high density 73 may be automaticallyselected under software control, as is exemplified by an exampleillustrated in FIG. 7D. For example, in some embodiment, it may bepreferable to divide the map of defects on a wafer into a plurality ofsmall regions, evaluate the density of defects in each region, andselect a region having defects of the highest density. Data defining theregion may be expressed by a set of coordinates data for each smallregion. If a polygonal region is generated, a set of the coordinates ofits vertices may be used to define the region. In the alternative, insome embodiment, it may be also preferable to use the density of defectswithin each small region as is, show difference in the density by regionin tone gradation 73, and generate a defects distribution image, forexample, like the one illustrated in FIG. 7E, thereby showing thedistribution of defects occurred all over the wafer.

When selecting a defects distribution on a defect map, it may be alsopreferable to use defect information received from the inspectionequipment or defect viewer (review station) (n−1) or (n−2). The defectinformation designates the type of defects such as, for example, patternformation defects which are illustrated in FIG. 8D (pattern defects;e.g., breaks (including near breaks) and short circuits (including nearshort circuits)), particles on the surface of a wafer which areillustrated in FIG. 8B, and scratches which are illustrated in FIG. 8Cas well as the extent of defects in terms of dimensions and area. Thedefect information would be helpful, in particular, for cases wheredefects resulting from different causes happen to have similardistributions or where different types of defects simultaneously occurfrom a plurality of causes, making it difficult to identify a defectsdistribution of which type of defects. For example, once the inspectionresults collection and analysis unit 30 classifies the inspectionresults received from the inspection equipment or defect viewer (reviewstation) (n−1) or (n−2) into defect types, defects distributionsaccording to the defect types can be obtained. By following the stepsS62 to S64, analysis for investigating the cause applies to each defecttype so that the accuracy of analysis can be enhanced by using defectsdistribution data from defects of the same type. All defectsdistributions thus obtained may be discretely analyzed or only thedefects or distributions of defects that are especially critical may beanalyzed. For these distributions, a region of high density of defectsshould be selected for analysis as illustrated in FIG. 7.

Then, how the inspection results collection and analysis unit 30executes a step 62 of evaluating a degree of matching, which ismentioned in FIG. 6, will be explained. In the step 61, the inspectionresults collection and analysis unit 30 evaluates a degree of matchingof the defect type and defects distribution selected as illustrated inFIG. 7 with the distributions of defects on wafers inspected in the pastwhich are stored in the past fault database 39. For example, supposethat a defects distribution DA1 in a region A having the defects to beanalyzed, as is illustrated in FIG. 9A, has been selected. For defectsdistributions RA1 to RAn on wafers inspected in the past, which areillustrated in FIG. 9B, obtain the density of defects in the selectedregion A and divide it by the density of defects on the whole surface ofthe wafer extracted for evaluation of matching with the past defects. Athus normalized value should be used as the index of a degree ofmatching. For the case illustrated in FIG. 9, evaluation is that thedefects distribution DA1 in the region A having the defects to beanalyzed matches with the defects distributions RA1 and RA3 on thewafers inspected in the past to a high degree.

In another embodiment where each of small regions on the wafer hasdifferent density of defects as illustrated in FIG. 7E, the inspectionresults collection and analysis unit 30 executes the same processing forthe data obtained from the past inspection and stored in the past faultdatabase 39. For the resultant two-dimensional data sequences, spatialcorrelation between the defects distribution selected for analysis andthe past defects distributions should be used as the degree of matching.Such two-dimensional data sequences can be treated in the same way asfor tone gradation images. By application of image pattern matchingtechniques, matching can be evaluated for cases where similar defectsdensity distributions occur at different positions. In this case, adegree of matching of similar patterns as well as discrepancy thereofcan be obtained. About how to evaluate matching for cases where similardefects distributions occur at different positions, embodiments thereofwill be described separately. By executing the described processingafter classifying the inspection results according to the defect typeand extent, as illustrated in FIG. 8, analysis can be performed moreexactly.

Then, how the inspection results collection and analysis unit 30executes a step 63 of selecting sub-processes as likely sources ofdefects, which is mentioned in FIG. 6, will be explained. As isillustrated in FIG. 10, the inspection results collection and analysisunit 30 first calculates a degree of matching of defects distributions(spatial correlations) DCa calculated in the step S62 for eachmanufacturing equipment that processed wafers having similar defectsdistribution ((P−1) to (P−3), (Q−1) to (Q−2), (R−1) to (R−3), . . . ).

As is shown in FIG. 10A, for a sub-process (P) in which the defectsdistribution to be analyzed occur only with a particular manufacturingequipment (P−1) used in the sub-process, only the causal equipment (P−1)has a high degree of matching DCa. By contrast, for a sub-process (Q)not having causal relationship, each manufacturing equipment used in thesub-process has similar distribution of values of the degree of matchingDCs, as is shown in FIG. 10B.

Then, the inspection results collection and analysis unit 30 sets asuitable threshold TH_(DCa) of the calculated matching (spatialcorrelation) degree DCa for the sub-processes (P to U). The inspectionresults collection and analysis unit 30 selects a manufacturingequipment that has the matching degree DCa more than the thresholdTH_(DCa) and processed the most wafers in each sub-process (amanufacturing equipment (P−1) in the sub-process (P) and a manufacturingequipment (R−3) in the sub-process (R)), calculates the number of wafersprocessed by the selected equipment as an evaluation value RV, andcompares the sub-processes. Consequently, high evaluation values RV areobtained in sub-processes (P and R) in which the manufacturingequipments have different values of matching degree DCa, that is,matching greatly differs, depending on the manufacturing equipment.Thus, the sub-processes (P and R) with high evaluation values RV and themanufacturing equipments ((P−1) and (R−3) that have the greatestmatching degree DCa in these sub-processes should be selected as likelysources of defects.

Particularly, if which wafer that suffered abnormal occurrence ofdefects is identified, a manufacturing equipment having a highevaluation value RV must have processed the wafer. Thus, manufacturingequipments that have a high evaluation value RV and processed thedefective wafer should be extracted as sub-processes and equipments aslikely sources of defects. The inspection results collection andanalysis unit 30 can implement the step S63 by calculating evaluationvalues RV for the sub-processes (P to U) as shown in FIG. 10C andselecting sub-processes having a high evaluation value RV as likelysources of defects.

Even if sub-processes as likely sources of defects have been selectedsuccessfully in the step S63, as illustrated in FIG. 10, there are manycases where a sub-process that is a real source of defects cannot bepinpointed. This is because there are cases where wafers are processedthrough a fixed combination of manufacturing equipments to be usedacross a plurality of sub-processes or where a plurality ofmanufacturing equipments are not in use in some sub-process. Forexample, if only one equipment that processes wafers is used in asub-process, its evaluation value RV shown in FIG. 10C might become highfrom the relationship by which a manufacturing equipment that has amatching degree DCa more than the threshold TH_(DCa) and processed themost wafers is extracted. Thus, the inspection results collection andanalysis unit 30 need to pinpoint a sub-process and relatedmanufacturing equipment as the source of defects by further executing astep S64 of analyzing cyclicity of matching degree data (a set of dataincluding a time factor) or its data sequence, which is mentioned inFIG. 6.

Then, how the inspection results collection and analysis unit 30executes the analysis method of the step 64 mentioned in FIG. 6 will beexplained.

If the cause of defects lies in a manufacturing equipment, the cycle ofoccurrence of defective wafers produced by the equipment differs,according to the type of the manufacturing equipment and the cause.These defective wafers occur in different aspects. For some defect type,the number of defective wafers increases and decreases, which showsapparent cyclicity as shown in FIG. 5A. For another defect type, thenumber of defective wafers changes in a long cycle from several days toseveral tens of days. For another defect type, once a defective waferoccurs, it continues to occur. For another defect type, the number ofdefective wafers gradually increases or decreases. For another defecttype, after defective wafers occurs for a period, the occurrencesuddenly stops. In the following, an embodiment example of processing aset or sequence of such data that varies differently (including a timefactor) will be explained.

First, the inspection results collection and analysis unit 30 checks theperiod of occurrence of the defects distribution to be analyzed,received from the inspection equipment or defect viewer (review station)(n−1) or (n−2). Specifically, this is implemented as follows. As isillustrated in FIGS. 11A, 11B, and 11C, after setting a suitablethreshold TH_(DCv) for a set or sequence of matching degree data(matching degree change) DCv, the period of occurrence of matchingdegrees more than the threshold TH_(DCv) should be checked. Using thedata following a point at which a matching degree more than thethreshold was first found, matching degree variation or fluctuation overtime is analyzed.

FIG. 11A shows a case of defects having cyclicity in which the matchingdegree DCv rises to a point and falls, which recurs. FIG. 11B shows acase of defects having cyclicity in which the matching degree DCv up anddown recurs every second wafer or each a plurality of wafers areprocessed. FIG. 11C shows a case of defects in which the matching degreeDCv varies without cyclicity. Any graphic representation in thesedrawings shows correlation between the matching degree variation orfluctuation and the structure or mode in which the manufacturingequipment processes wafers or the process conditions.

In this relation, the inspection results collection and analysis unit 30carries out the analysis of matching degree variation or fluctuation intwo stages. In the first stage, check is made for cyclicity of defectsacross the wafers processed concurrently or sequentially by the processand related equipment and the matching degree data for the wafersarranged in sequence in which they were processed is analyzed formatching degree variation or fluctuation. If a set of sequence of suchdata is available, frequency analysis should be performed for the data,using a FFT or the like. If not, an average of matching degree data foreach of the wafers processed under the same conditions should beobtained and frequency analysis performed for a sequence of the thusobtained data. For example, if 25 wafers in a lot are processedsequentially by equipment, matching degree data for same-numbered wafersis averaged to generate a sequence of 25 data for which frequencyanalysis then should be performed. From the results of analysis, byselecting a manufacturing equipment for which the cycle in which itprocesses wafers corresponds to the cycle of variation or fluctuation ofmatching degree DVc, it is possible to pinpoint a manufacturingequipment that caused the defects of interest.

The cycle in which equipment processes wafers will be explained below.In the second stage, check is made for matching degree variation orfluctuation during a longer period, that is, variation of fluctuation ofoccurrence of defective wafers for several days is checked. For example,as is shown in FIG. 12, the matching degree evaluation results forwafers processed by an equipment are arranged in sequence of days whenthe equipment processed the wafers and check is made for data of highmatching degrees. For example, by selecting data of matching degreeshigher than a certain threshold THDCv or finding a highest value in datasequence, check for a cycle of occurrence of wafers having high matchingdegrees. If such wafers occur intermittently as in the example shown inFIG. 12, judge whether they occur at certain intervals ornon-periodically. Using the records of a manufacturing equipment such asfabrication process logs and maintenance logs received from thefabrication line management station 34, check to see whether the timewhen the equipment processed the above wafers corresponds to themaintenance cycle or time before or after the maintenance. If so, theprocess by the equipment is likely to cause the defects and thus theequipment is extracted as a likely source of defects. If the wafershaving high matching degrees occur non-intermittently, an equipment forwhich maintenance was performed at time corresponding to the time whensuch wafers begin to appear is extracted as a likely source of defects.By executing this analysis for all equipments in the sub-processesselected as likely sources of defects, the spectrum of extractingequipments as likely sources of defects can be further narrowed down.

If the inspection results collection and analysis unit 30 cannotdetermine a sub-process and related manufacturing equipment that causeddefects in the step 65 of FIG. 6 in the manner described above, it goesto a step S66. Despite the attempts to pinpoint causal sub-processes, itmay be impossible to pinpoint a particular sub-process and relatedmanufacturing equipment because the inspected wafers are few or becausesufficient frequency analysis cannot be performed. In such cases, toidentify which of the sub-processes remaining as likely sources of thedefects, perform additional inspection with the inspection equipment(n−1) or review station (n−2). If it is found that wafers belonging tothe same lot of the wafer on which abnormal occurrence of defects wasdetected are not yet inspected, based on the data from the fabricationhistory management unit 32, the inspection results collection andanalysis unit 30 first instructs the inspection equipment (n−1) orreview station (n−2) to execute inspection of the remaining wafers inthe same lot. By executing cyclicity analysis of the matching degreesDCv of the remaining wafers in the lot obtained from the additionalinspection, a particular manufacturing equipment can be identified asthe source of the defects with a high probability, based on the units ofwafers processed by equipment as in the above-described embodiment.Therefore, the same lot is first inspected, if the inspection processhas sufficient throughput.

For the inspection equipment that is not high throughput, it isdifficult to inspect all remaining units in the same lot. In such caseswhere cyclicity analysis cannot be performed, the inspection resultscollection and analysis unit 30 selects the wafers processed only withone of the plurality of manufacturing equipments of likely sources andinstructs the inspection equipment (n−1) or review station (n−2) toinspect the selected wafers, so that it can determine a causalsub-process efficiently. Referring to FIG. 13, for example, suppose thattwo manufacturing equipments ((n−3)−2) and ((n−2)−2) which are shadedare likely sources of defects. Suppose that wafers A were fed andprocessed by the manufacturing equipments, as indicated by thin arrows,and wafers B were fed and processed by the manufacturing equipment, asindicated by bold arrows. If the wafers A processed by bothmanufacturing equipments ((n−3)−2) and ((n−1)−2) are inspected, a causalprocess cannot be determined. However, if the inspection resultscollection and analysis unit 30 instructs the inspection equipment (n−1)or review station (n−2) to inspect the wafers B processed only by themanufacturing equipment (n−3)−2, it can determine the equipment (n−3)−2as the source if the matching degrees of the inspected wafers are highand the equipment ((n−1)−2) as the source if the matching degrees of theinspected wafers are low, using the results of this inspection.

If there are three or more manufacturing equipments as likely sources,the inspection results collection and analysis unit 30 instructs theinspection equipment (n−1) or review station (n−2) to inspect wafers inthe following procedure. First, inspect first wafers processed with ahalf of the manufacturing equipments of likely sources. If theinspection results collection and analysis unit 30 judges the matchingdegrees of the inspected first wafers high, inspect second wafersprocessed with a half of the manufacturing equipments that processed thefirst wafers. If the inspection results collection and analysis unit 30judges the matching degrees of the inspected first wafers low, inspectthird wafers processed with the remaining half of the manufacturingequipments of likely sources. Repeat the above inspection process. Ifthere remains a plurality of sub-processes as likely sources in whichone equipment processed the wafers, the inspection results collectionand analysis unit 30 would determine a causal sub-process by instructingthe inspection equipment to inspect the wafers processed by intermediatesub-processes. The inspection results collection and analysis unit 30can select the wafers to be inspected, based on the fabrication processdata per wafer received from the fabrication history management unit 32.If wafers in other lots are inspected, it is desirable to usesame-numbered wafers as the wafer on which the critical defects occurredand the source thereof is being sought.

Once the inspection results collection and analysis unit 30 hasdetermined a manufacturing equipment that caused the defects byexecuting the steps 61 through 66 mentioned in FIG. 6, it stops thefabrication process by the causal manufacturing equipment and outputsinstructions to do troubleshooting and maintenance to the display andannunciator means 38 via the fabrication line management station 34 andthe fault monitoring unit 36. Thereby, at least the sub-processperformed by the causal manufacturing equipment stops and furtherfabrication of unacceptable wafers can be avoided.

As described hereinbefore, for wafers flowing through the sub-processesalong the production line, the inspection results collection andanalysis unit 30 is able to infer what sub-process and relatedmanufacturing equipment caused critical defects through the followingsteps: receiving the defects distribution or defects variation data 31per type of defects occurred to the wafers inspected by the inspectionequipment (n−1) or review station (n−2); collecting the above defectsdistribution or defects variation data 31 per defect type, thefabrication process data 33 per wafer, sent from the fabrication historymanagement unit 32, and the equipment data 35 that representsmanufacturing conditions including the configuration and history of eachmanufacturing equipment, sent from the fabrication line managementstation 34; examining the collected defects distribution or defectsvariation data 31 per defect type for matching to what degree with amultiplicity of the past inspection results stored in the past faultdatabase 39; and analyzing defects data, using the fabrication processdata 33 per wafer and the equipment data 35 in combination.

The above-mentioned fabrication process data 33 per wafer is fabricationhistory data describing when a wafer was processed by what manufacturingequipment. The above-mentioned equipment data 35 describes units ofwafers that the particular manufacturing equipment processes.

In order to implement the above-described processing method, thefabrication process data describing when a wafer was processed by whatmanufacturing equipment and the equipment data describing units ofwafers that the particular manufacturing equipment processes areessential. Therefore, the inspection results collection and analysisunit 30 receives the defects data 31 per wafer from the inspectionequipment or review station and infers what manufacturing sub-processand related manufacturing equipment caused the detected defects, usingthe defects data in combination with the fabrication process data 33 perwafer, sent from the fabrication history management unit 32, and theequipment data 35 representing the configuration and conditions of theequipment, sent from the fabrication line management station 34, as isshown in FIG. 13. By the analysis method according to the invention,once a faulty equipment has been determined, linked action of thecontrol devices can stop the operation of that equipment and the faultmonitoring unit 36 can give the alarm to the line administrator 37.Moreover, instructions to perform additional inspection of other wafersnot yet inspected can be issued as required.

In the process of analysis, check is made for causal relationship of thestructure of a manufacturing equipment and the number of wafers to beprocessed at a time by the equipment to the cycle in which defectivewafers occur as shown in the example illustrated in FIG. 5. For thispurpose, it is convenient to register in advance such information abouteach manufacturing equipment into a database like a table illustrated inFIG. 14 that is stored in a memory of the fabrication line managementstation 34. In the table shown in FIG. 14, the “process cycle” means thecycle in which wafers are processed under the same conditions.

For some manufacturing equipments, for example, the CVD equipment havingtwo chambers and the CMP equipment having a plurality of heads, shown inFIG. 5C, the parts in use for process and the conditions differ,depending on the wafers to be loaded. For the process cycle of suchequipments, specify the cycle in which wafers are processed by the sameparts. For an equipment that processes a few lots of wafers at the sametime, like the furnace shown in FIG. 5B, because the wafers are set indifferent positions in the furnace, specify the number of wafers to beprocessed at the same time as units of wafers. The “number of wafers tobe processed at a time” in the table shown in FIG. 14 means the numberof wafers that are normally processed sequentially at the same time orat a time. Because most equipments process one lot of waferssequentially at a time, specify the number of wafers in one lot in the“number of wafers to be processed at a time” column. For an equipmentlike the furnace shown in FIG. 5B, if it processes, for example, 100wafers at the same time, specify “100.” As is illustrated in FIG. 14, itis preferable to record maintenance information for each manufacturingequipment also so that the source of defects can be inferred from thecausal relationship between the maintenance and the occurrence of thedefects. As is illustrated in FIG. 14, the table contains the entries ofsub-process, manufacturing equipment name, process cycle, number ofwafers to be processed at a time, and maintenance record which are usedas the equipment data 35 for each manufacturing equipment.

Then, how the inspection results collection and analysis unit 30evaluates the degree of matching in the step 62 mentioned in FIG. 6 willbe explained in further detail. Varieties of defects actually occur tosemiconductor wafers and matching of the distribution of detecteddefects with past similar defects distributions may not be wellevaluated by simply comparing the defects distribution in a region andthe past defects distributions in the corresponding region asillustrated in FIG. 9.

FIG. 15 shows examples of defects distributions such as pattern defects,particles, and scratches. In the defects distributions illustrated inFIGS. 15A, 15B, and 15C, high-density defects exist locally on thewafer. Especially, a narrow region in which a great number of defectsexist together is generally called a cluster 151 as is shown in FIG.15A.

In the defects distribution illustrated in FIG. 15C, a great number ofdefects disperse all over the wafer and this is thought to be scatteredformation of the defects-dense region in FIG. 15B. The defectsdistributions illustrated in FIGS. 15D and 15E are examples that regulardefects distributions exist within chips or shots besides thedistribution over the wafer.

The defects distribution illustrated in FIG. 15D occur due to a fault ofa pattern-forming mask such as a reticle error or a trouble with anexposure equipment. As compared defects caused by other manufacturingequipments, the dimensions of defects are smaller and defects occur inthe corresponding position across the shots.

In another example of defects distribution as illustrated in FIG. 15E, achip or shot has a defects-dense region in defects distribution withinit and the distribution of the chips having such region within the chip(shot) takes a particular form over the wafer. This is due to that aportion of the wafer that is structurally susceptible to processvariation is damaged, affected by an incorrect process that is evenlyapplied to the chips on the wafer. In such cases, even similar defectsdistributions having the same region where defects occur within thewafer, for example, the defects distribution illustrated in FIG. 15E(similar distributions exist within chips) and the defects distributionillustrated in FIG. 15 (defects randomly occur, independent of thechips) are considered as different modes and should be treatedseparately. In some defects distributions as in FIG. 15E, similardistributions exist within chips, but the defects distribution over thewafer do not have distinctive features.

As illustrated above, varieties of defects distributions occur,according to the cause of defects. For example, the cluster 151 shown inFIG. 15A where a local region on the wafer is affected and otherdistributions of defects that affect a broader area over the wafer arethought to be caused by different sources. Sometimes, it may happen thatthese defects occurrence modes coexist on one wafer. In order toproperly infer a causal sub-process and related manufacturing equipmenteven for cases where defects of different modes coexist on the wafer,the above-described matching degree evaluation in the step S62 mentionedin FIG. 6 needs elaboration.

Then, an exemplary procedure of such evaluation for cases wheredifferent defects distributions coexist on the wafer, which is carriedout by the inspection results collection and analysis unit 30, will beexplained, using FIG. 16. Part or all of this evaluation procedure maybe carried out by the inspection equipment or review station.

The inspection results collection and analysis unit 30 executes thesteps according to the procedure illustrated in FIG. 16 so that defectsdistributions can be sorted as illustrated in FIG. 15. Because thecluster 151 is especially high-density defects, it is likely to make theanalysis for other defects distributions difficult. Thus, in the firststep S161, detect a cluster 151 of defects. Judging whether there is acluster of defects should be done as follows. Measure the distance oftwo defects points (this point is the center of gravity of a defect orthe like) for all defects. If defects for which the measure distance isshorter than a certain distance crowd together, the defects are regardedas a cluster. To facilitate this, a clustering method used in theconventional pattern recognition field can be applied.

If there is a cluster of defects by the judgment of step S161, judgewhether the cluster of defects is cyclic across shots/chips in stepS162. If the cluster of defects is not cyclic across shots/chips (thecluster of defects occurs discretely), extract the cluster of defects instep S163. Thus, a discrete cluster of defects S163 can be extracted.

In step S164, then analyze defects that fall under the cluster ofdefects judged cyclic across shots/chips in the step S162 and remainingdefects distributions other than the discrete cluster of defectsextracted in the step S163. That is, if similar clusters of defectsoccur across shots (in units of shots) or chips by judgment of the stepS162, handle them as a defects distribution coming under thedistribution pattern illustrated in FIG. 15(e) and examine how thedefects occur over the wafer in the step S164.

In this way, in the steps S161 through S163, the cluster of defects thatis not cyclic across chips or shots can be separated from other defects.

In the step S164, then check the remaining defects other than theclusters of defects to see whether the defects are cyclic across chipsor shots. In the following, checking cyclic defects across chips will beexplained; the same description applies to those across shots. In stepS16, first merge all chips defects data and examine defectsdistributions within the chips in the same manner for defectsdistributions on the wafer illustrated in FIG. 9. For example, dividethe chip area into small regions. If a region having especiallyhigh-density defects exists, evaluate the density of defects in thecorresponding region within all chips. Examine whether the high-densitydefects region occurs within a particular chip or the similar regionexists within a plurality of chips also. If the similar high-densitydefects state occurs to a plurality of chips, it is judged that thedefects are cyclic across the chips. Evaluate other small regions in thesame way. If another region has similarity to the region evaluatedbefore, join these regions. Thereby, a defects distribution within achip that is cyclic across chips (for example, defects distribution Asin a region having most defects within the shot (chip)) can be obtained.

In step S166, compare each chip or shot with the thus obtained defectsdistribution within a chip or shot (for example, defects distribution Asin a region having most defects within the shot (chip)) to determine thedegree of matching in the same manner as for the defects distributionson the wafer illustrated in FIG. 9. Thereby, the defects distributionwithin a chip or shot and the distribution of the chips or shots havingthe above defects distribution within it can be obtained. Using thesedistributions, in step S167, examine whether defects or clusters ofdefects that are cyclic across chips (shots) show a characteristicon-wafer distribution. If there is a characteristic on-waferdistribution (defects occurring locally in a portion of the wafer suchas bias), generate defects distribution Aw in a region having mostdefects on the wafer in step S168. Thereby, defects cyclic across shots(chips) and clustering in a region on the wafer 173 can be extracted.Unless there is a characteristic on-wafer distribution, defects cyclicacross shots (chips) 172 can be extracted.

If the defects are not cyclic across chips or shots by judgement of thestep S164, what to do is examining on-wafer defects distributions asillustrated in FIG. 9. That is, in step S169, examine whether thedefects show a characteristic on-wafer distribution. If there is acharacteristic on-wafer distribution, generate defects distribution Awin a region having most defects on the wafer in step S170. Thereby,defects 175 clustering in a region on the wafer can be extracted. Unlessthere is a characteristic on-wafer distribution, defects in numerousdefects mode (random) 174 can be extracted.

The defects distributions data obtained in the above-described mannershould be recorded in storage 42 together with the results of inspectionof wafers received from the inspection equipment or review station.

FIG. 17 shows an exemplary data record (unit) to be stored in thestorage 42, primarily consisting of defects data 178 and defectsdistributions data 179. The particulars of the exemplary dataillustrated in FIG. 17 are as follows: wafer type, sub-process, lotnumber, wafer number, inspection equipment name, inspection conditions,etc. which are specified per data record (unit); defects data consistingof defect coordinates data (defect 1 to N: coordinates, dimensions,category, etc.) 178; a number of clusters (cluster 1, 2, . . . ) eachconsisting of region, area, density, . . . , category A (for example,particles); on-chip distribution of defects consisting of region, area,. . . , category B (for example, pattern defects) 176; and on-waferdistribution of defects consisting of region, area, etc. 177. Categorydata describing the type of defects is assigned to each cluster ordefects distribution. A region may be defined by a set of the verticesof a polygonal or its visual image in tone gradation may be generated asillustrated in FIG. 17.

While general handling of defects distributions was describedhereinbefore, it is desirable to separate reticle errors from otherdefects because the reticle errors, unlike other defects, occur in allshots of all wafers whenever they occur and cannot be removed untilremedy action is taken. The reticle errors occur in all shots on a waferand in the corresponding coordinates within the shots. Once a point atwhich the reticle error often occurs has been detected, the reticleerrors are easy to recognize and should be removed before the start ofthe procedure illustrated in FIG. 16.

In the manner described hereinbefore, it is possible to roughly dividedefects distributions on a wafer into clusters, on-chip or on-shotdistributions, and on-wafer distributions, and obtain their shape andthe density of defects within a region.

Based on the above-mentioned defects distributions data, by comparingthe distribution of the detected defects with the defects distributionson the wafers inspected in the past and quantitatively evaluating thedegree of matching (similarity), according to the steps S62 through S66illustrated in FIG. 6, the inspection results collection and analysisunit 30 can determine the source of the defects. A plurality of defectsdistributions may take place on one wafer. In that event, analysisshould be performed for each defects distribution. An advantage of theinvention is that, by analyzing types of defects distributions in theabove-described manner, investigating the cause of the detected defectscan be performed certainly even if different types of defects resultingfrom different causes coexist on a wafer.

Some defects distributions result from the same cause, but do not occurin exactly the same position on the wafer. For example, if amanufacturing equipment is faulty, causing defects in the lower leftportion of the wafer, a great number of defects would occur near thelower left rim of the wafer as is illustrated in FIGS. 18A and 18B. Inthis case, there is a possibility that the defects appear in somewhatdifferent positions as shown in FIGS. 18A and 18B, though they resultfrom the same cause. By contrast, defects that occur in the center ofthe wafer as is illustrated in FIG. 18C are thought to be result fromanother cause. Thus, when evaluating the degree of matching (similarity)between defects distributions, it is desirable to evaluate the sameforms of defects distributions high in matching (similarity) even ifoccurring in somewhat different positions.

In some embodiment, it may be preferable that the inspection resultscollection and analysis unit 30 calculates the degree of matching(similarity) between defects distributions in the corresponding positionas well as those displaced therefrom. Use a distance function as aweight function (factor). A greater value of the distance function isassigned to a region of defects that is nearer to the position where thehighest degree of matching between defects distributions is evaluated.For a region, after determining a degree of matching (similarity)between defects distributions, multiple the degree by its weightfunction (factor). Thus, final evaluation of matching (similarity)between defects distributions in a region should be performed.

For example, let us to determine which of the regional distributionshown in FIG. 18B and that shown in FIG. 18C has a higher degree ofmatching (similarity) to the distribution shown in FIG. 17A. Multiplythe obtained matching degree for each distribution by the weightfunction (factor) that decreases as the distance from the referenceposition increases. Because the distribution of FIG. 18B is nearer tothe distribution of FIG. 18A than the distribution of FIG. 18C, itsmatching degree is multiplied by a greater weight function (factor).Hence, evaluation is that the distribution of FIG. 18B has a higherdegree of matching (similarity) than the distribution of FIG. 18C. Foron-shot or on-chip defects distributions, matching should be evaluatedby alignment on a X-Y plain. For on-wafer defects distributions,matching should be evaluated by matching in the radius direction and therotational direction in angles because wafers are circular. Usually,locally occurring defects, for example, scratches due to a fault of theloader, often result from a cause in the vicinity of the location of thedefects and they generally appear in the corresponding position on allchips or wafers; in short, they have comparatively high repeatability.By contrast, defects resulting from an indirect cause, for example,particles from the source thereof at a distance from the place wherewafers are processed, affect a wider area and, generally, do not appearin the corresponding position on all chips or wafers; in short, theyhave low repeatability. Reticle errors are thought to appear in thecorresponding position on all chips and are detected in position asaccurately as the precision of the coordinates for defect detection ofthe inspection equipment.

In general, appearance of defects distributions in the correspondingposition on all chips or wafers, in short, the repeatability of defects,depends on the extent of the region where the defects occur. In viewhereof, an extent factor should be added to the above-mentioned weightfunction (factor) that changes the value of degree of matching(similarity), according to the position of the region of defects. Forexample, project defects on suitable coordinate axes and measure thelength of a region of the projected defects. According to the projecteddefects region length, the function (factor) should be assigned so thatthe weight greatly decreases when the distance of the defectsdistribution from the reference position is longer and the projecteddefects region length is shorter.

As the coordinate axes, those representing the radius andcircumferential directions should be used for on-wafer distributions asdescribed above. In the case of FIG. 18, the distribution of FIG. 18Aextends in the circumferential direction. In view hereof, the weightfunction (factor) should be set so that the matching degree does notdecrease if displacement in the circumferential direction is greaterthan displacement in the radius direction.

In the embodiment described hereinbefore, the analysis method wasdiscussed, assuming that sufficient inspection results are available.However, for a fabrication line for a production mode that accommodatesa variety of wafer products and a small quantity of each type products,it may be difficult to obtain a sufficient amount of data for same typeproducts. In such cases, analysis should be performed, using data fordifferent types of wafers in combination. If the conditions ofinspection (defect detection sensitivity) differ, depending on the typeof wafers to be evaluated, evaluation should be performed in thefollowing manner. Set referential inspection sensitivity in advance;inspect the same wafers by the referential inspection conditions andordinary inspection conditions; and obtain a ratio of the defect countunder ordinary conditions to the defect count obtained under thereferential conditions. Using this ratio as an inspection sensitivitycorrection coefficient, convert the number of detected defects into thedefect count that is obtained under the reference conditions. Foron-shot or on-chip defects distributions, comparison is impossiblebetween different types of wafers. If a variety of wafers are inspected,comparison is made between on-wafer defects distributions without regardto on-shot or on-chip distributions.

The procedure illustrated in FIG. 6 assumed that a causal equipment issought from among all sub-processes prior to the inspection process. Fora detected wafer on which a great number of defects occurred, if a waferhaving similar distribution of a great number of defects is not found inthe inspection processes prior to the sub-process where the defectivewafer was found, a sub-process that caused the defects is likely to fallbetween the inspection process where the defective wafer was first foundand the preceding inspection process, that is, the last sub-processwhere no defective wafers were found.

In this case, the inspection results collection and analysis unit 30first performs analysis for the manufacturing equipments existingbetween the inspection process where the defective wafer was first foundand the preceding inspection process, using equipment data 35,fabrication process history data 33, and the like. Thereby, analysis canbe carried out with a relatively small amount of calculation. If,however, a causal sub-process failed to be identified, additionalevaluation is performed, using the data for the sub-processes prior tothe examined stage. This is because a faulty sub-process causing defectsand a sub-process where the defects actually occur due to the faultyprocess are not always identical. For example, a deposition sub-processinvolves a fault and, in a later heat treatment sub-process, defectsoccur to only wafers processed by the faulty deposition process.

Using the results of analysis of defects for finding out the cause ofthe defects through the data processing performed by the inspectionresults collection and analysis unit 30 according to the invention, amethod of managing the fabrication process will then be described. Asillustrated in the above-described embodiment, the method of theinvention can ascertain what caused defects. However, some source ofdefects is likely to cause similar defects to recur.

In view hereof, the inspection results collection and analysis unit 30is able to retrieve the occurrence states of defects in the same mode ofthe once isolated cause of defects from the database and output to thedisplay device 40, display and annunciator means 38, and fabricationline management station 34. Monitoring this information, the operator oradministrator can take measures and prevent similar defects recurrence.The inspection results collection and analysis unit 30 stores instancesof defects obtained through analysis, which are illustrated in FIG. 19(regarding CMP and etching equipments), as a database, in its storage42. As defect instances data, defect type, causal wafer sub-process andequipment, features of defects such as dimensions and distribution,cyclicity of defects, day/time when defects occurred in the past, etc.are stored. If the cause was isolated and revealed, the cause and remedymethod should be stored with the above-mentioned data so that theoperator or administrator can quickly cope with similar defects if recurin future.

Among the defects occurred in the past, the inspection resultscollection and analysis unit 30 registers in advance, particularly,defects that are anticipated to recur as recurrence mode defects intothe storage 42 so it can monitor the recurrence mode defects and givethe alarm to the line administrator via the fault monitoring unit 36 andthe display and annunciator means 38 when the occurrence of similardefects of similar distributions is detected. Once recurrence modedefects have been registered, the inspection results collection andanalysis unit 30 can detect defects of similar mode and prevent frequentoccurrence of defects of this mode. Occurrence of similar defectsdistributions should be detected in the following manner. Set athreshold, based on the matching degrees for the manufacturingequipments in which no trouble has occurred. If the matching degree ofsimilar defects exceeds the threshold, give the alarm to theadministrator. In this way, the inspection results collection andanalysis unit 30 monitors the recurrence mode defects registeredbeforehand. Thereby, a constantly stable fabrication process can berealized and a constant good yield can be expected. Remedy actions thathave previously been taken against such defects should be registeredwith the defects so that the operator or administrator can quickly copewith the defects if recur. The downtime of a causal manufacturingequipment can be minimized.

While the system configuration according to a preferred embodiment ofthe invention shown in FIG. 3 is an example of a whole fabrication lineincluding inspection equipment, the functions of analyzing defectsdistributions and cyclicity may be installed in a standalone inspectionequipment. The standalone inspection equipment is capable of cyclicityanalysis for defects occurred to wafers in lots and outputs photographsof defects with defects distributions marked thereon, graphs of matchingdegree data, cyclicity analysis results to the display device connectedto it so that personnel who has knowledge of the characteristics orproperties of the manufacturing equipments can infer a causalmanufacturing equipment. In some embodiment, it may be possible toregister data of the process units of all manufacturing equipments onthe inspection equipment and provide the inspection equipment with afunction of presenting manufacturing equipments as likely sources ofdefects that have a process cycle corresponding to the cycle as theanalysis result. In this way, by providing the inspection equipment withthe analysis function, analysis results can be obtained immediatelyafter inspection. In other words, the inspection equipment may befurnished with the inspection results collection and analysis unit 30.

Furthermore, it is preferable to store device layout data into thestorage 42 beforehand so that the inspection results collection andanalysis unit 30 can investigate the cause of defects more easily byusing defects distributions and device layout data in combination.

The inspection results collection and analysis unit 30 displays defectsdistributions on the display device 40, for example, a defectsdistribution 200 which is shown in FIG. 20, where a defects distributionobtained is superposed on actual layout of on-chip (on-shot) patterns.This enables the operator to know which portion of the layout ofpatterns is susceptible to defects and may help perform in-depthanalysis for finding out the source of the defects. To display layoutdata with defects distributions, means for getting design data arenecessary. In some embodiment, it is desirable to configure theinspection results collection and analysis unit 30 so that it can getdesign data from a design data server.

The inspection results collection and analysis unit 30 displays not onlyan on-chip defects distribution 200, but also an on-wafer defectsdistribution to make the defects easy to locate. It is preferable tochange the colors of the chips on the wafer, according to the degree ofmatching with the on-chip defects distribution 200, so that the operatorcan easily and visually distinguish a portion of the wafer where thedefects have on-chip defects distributions from the remaining portionwhere defects are distributed. If a plurality of different defectsdistributions coexist on a wafer, it is preferable to show them indifferent colors and shapes on the display device 40 and presentanalysis results of one of the distributions selected by the operator.

The inspection results collection and analysis unit 30 also displaysprocesses as likely sources of defects obtained by analysis in a list ofevaluation values 202 on the display device 40 so that the operator canknow which processes have especially high evaluation values. It ispreferable to enable the operator to select one of the processes andcheck a list of matching (similarity) 203 degrees of the equipments usedin that process. The inspection results collection and analysis unit 30displays a graph of matching (similarity) degree data sequence 204 forthe sub-process and related manufacturing equipment selected by theoperator, together with related data including the equipment name thatprocessed the wafers, a cycle of defects occurrence obtained from theanalysis results, a cycle of process judged corresponding to the cycleof defects occurrence, and date/time or cycle of maintenance, so thatthe operator can easily judge validity of the analysis results.

The method of investigating the cause of defects according to theinvention can be applied equally to, in addition to semiconductor waferproducts, products that are manufactured through a plurality ofprocesses, for example, display devices such as TFT and plasma displayand magnetic heads for use in magnetic recording devices.

Even for cases where there are less inspection processes than in thesemiconductor manufacturing process, for example, a factory where onlycomplete product inspection is performed before shipment, from thehistory of equipment that processed defective products, it is possibleto pinpoint a sub-process and related manufacturing equipment in thesame way as described hereinbefore with the preferred embodiment.

According to the invention, using the defect inspection results, amanufacturing equipment that causes abnormal defects can be determinedquickly and easily. The benefit hereof is preventing the production linefrom outputting a great amount of unacceptable products due to amanufacturing equipment fault.

Furthermore, advantage of the invention is process variation managementthat can prevent the production line from frequently outputtingunacceptable products due to a manufacturing equipment fault.

The invention may be embodied in other specific forms without departingfrom the spirit or essential characteristics thereof. The presentembodiment is therefore to be considered in all respects as illustrativeand not restrictive, the scope of the invention being indicated by theappended claims rather than by the foregoing description and all changeswhich come within the meaning and range of equivalency of the claims aretherefore intended to be embraced therein.

What is claimed is:
 1. A method for manufacturing semiconductor devicescomprising: an inspection step of inspecting semiconductor substratesprocessed until a predetermined sub-process for defects or suspecteddefects occurred to the semiconductor substrates, with an inspectionequipment, in a fabrication line consisting of a plurality ofsub-processes arranged in sequence, each sub-process being performed byone or a plurality of manufacturing equipments; and a step of collectingand analyzing inspection results, wherein said step of collecting andanalyzing inspection results comprising: a step of generating a defectsdistribution on a semiconductor substrate in order to investigate thecause of the defects, based on the inspection results obtained from saidinspection step; a step of quantitatively evaluating defect features ofthe defects distribution on a semiconductor substrate generated by saidstep of generating a defects distribution; a step of generating data ofthe defect features quantitatively evaluated by said step ofquantitatively evaluating defect features for the manufacturingequipments that processed the semiconductor substrates and thesub-processes that applied to the semiconductor substrates; and acausation inferring step of evaluating cyclicity of the defect featuresdata for the manufacturing equipments that processed the semiconductorsubstrates and the sub-processes that applied to the semiconductorsubstrates generated by said atop of generating data, thereby inferringa causal manufacturing equipment that caused the defects.
 2. A methodfor manufacturing semiconductor devices according to claim 1, furthercomprising a step of taking remedy action for the causal manufacturingequipment or sub-process that caused the defects, inferred by saidcausation inferring step in said step of collecting and analyzinginspection results.
 3. A method for manufacturing semiconductor devicesaccording to claim 1, wherein said step of generating a defectsdistribution in said step of collecting and analyzing inspection resultsincludes a step of specifying in advance a detects distribution on asemiconductor substrate.
 4. A method for manufacturing semiconductordevices according to claim 1, wherein said step of generating a defectsdistribution in said step of collecting and analyzing inspection resultsincludes a step of specifying in advance a defect type and a defectsdistribution on a semiconductor substrate.
 5. A method for manufacturingsemiconductor devices according to claim 1, wherein said causationinferring step in said step of collecting and analyzing inspectionincludes a step of identifying a manufacturing equipment whose cycle ofprocessing semiconductor substrates nearly corresponds to said cyclicityof the defect features data.
 6. A method for manufacturing semiconductordevices comprising: an inspection step of inspecting semiconductorsubstrates processed until a predetermined sub-process for defects orsuspected defects occurred to the semiconductor substrates processeduntil a predetermined sub-process for defects or suspected defectsoccurred to the semiconductor substrates, with an inspection equipment,in a fabrication line consisting of a plurality of sub-processesarranged in sequence, each sub-process being performed by one or aplurality of manufacturing equipments; and a step of collecting andanalyzing inspection results, wherein said step of collecting andanalyzing inspection results comprising: a step of generating a defectsdistribution on a semiconductor substrate in order to investigate thecause of the defects, based on the inspection results obtained from saidinspection step; a step of quantitatively evaluating defect features ofthe defects distribution on a semiconductor substrate generated by laidstep of generating a defects distribution; a step of generating data ofthe defect features quantitatively evaluated by said step ofquantitatively evaluating defect features for the manufacturingequipments that processed the semiconductor substrates and thesub-processes that applied to the semiconductor substrates; and acausation inferring step of evaluating correlations between said defectfeatures and said manufacturing equipments that processed thesemiconductor substrates for each sub-process that applied to thesemiconductor substrates, based on the defect features data for themanufacturing equipments that processed the semiconductor substrates andthe sub-processes that applied to the semiconductor substrates,generated by said step of generating data, and selecting a sub-processof high correlation, thereby inferring a causal manufacturing equipmentthat caused the defects.
 7. A method for manufacturing semiconductordevices according to claim 6, further comprising a step of taking remedyaction for the causal manufacturing equipment or sub-process that causedthe defects, inferred by said causation inferring step in said step ofcollecting and analyzing inspection results.
 8. A method formanufacturing semiconductor devices according to claim 6, wherein saidstep of generating a defects distribution in said step of collecting andanalyzing inspection results includes a step of specifying in advance adefects distribution on a semiconductor substrate.
 9. A method formanufacturing semiconductor devices according to claim 6, wherein saidstep of generating a defects distribution in said step of collecting andanalyzing inspection results includes a step of specifying in advance adefect type and a defects distribution on a semiconductor substrate. 10.A method for manufacturing semiconductor devices according to claim 6,wherein said step of quantitatively evaluating defect features in saidstep of collecting and analyzing inspection results includes a step ofevaluating similarity of the defects distribution on a semiconductorsubstrate generated by said step of generating a defects distribution todefects distributions respectively observed on a plurality ofsemiconductor substrates inspected in the past.
 11. A method formanufacturing semiconductor devices according to claim 6, wherein, insaid step of evaluating similarity, the similarity is evaluated withregard to defects distributions of nearly the same defect type.
 12. Amethod for manufacturing semiconductor devices according to claim 6,wherein said causation inferring step in said step of collecting andanalyzing inspection includes a step of identifying a manufacturingequipment whose cycle of processing semiconductor substrates nearlycorresponds to said cyclicity of the defect features data.